NSA method has an extensive competition with another alternate approach, which can also work with only self data.
One class classifications (OCC) solve problems where the training datasets only contain samples of one class, and learning models have to identify new data whether belong to that class or not. it is also known as unary classification or class-modelling problem. Most common approach for solving one class problem is one class support vector machine (OCSVM[1]}). Other similar approaches are Minimum Co-variance Method (MCM) [2]}, Gaussian Mixture Model (GMM)[3]}, Dirichlet Process Mixture Model (DPMM)[4]}, Kernel Density Estimator (KDE)[5]}, Robust KDE[6]}. GWR-Netwrok[7]}, Deep Support Vector Data Description (SVDD), Deep Auto Encoder based Methods[8]}, Generative Adversarial Net Based approaches (eg: [9]}, [10]}, etc.
These OCC techniques can be classified in 6 types. Minimum Covariance Determinant(MCD[11]}), OCSVM, Deviation-based(LMDD[12]}) considered as linear model based OCC techniques. Another type is proximity based which includes
Local Outlier Factor(LOF)[13]}, Connectivity-Based (COF[14]}), Clustering-Based LOF(CBLOF[15]}), Histogram-based (HBOS[16]}), K-Nearest Neighbors (kNN[17]}), Subspace Outlier Detection( SOD [18]}).
